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. 2018 Mar;10(3):a032102. doi: 10.1101/cshperspect.a032102

Do Memory CD4 T Cells Keep Their Cell-Type Programming: Plasticity versus Fate Commitment?

Complexities of Interpretation due to the Heterogeneity of Memory CD4 T Cells, Including T Follicular Helper Cells

Shane Crotty 1,2,3
PMCID: PMC5830898  PMID: 28432129

Abstract

Plasticity is the ability of a cell type to convert to another cell type. There are multiple effector CD4 T-cell subtypes, including TH1, TH2, TH17, TH1*, CD4 CTL, TH9, and TFH cells. It is commonly thought that a CD4 T cell can readily show full plasticity—full conversion from one differentiated cell—and this propensity to plasticity is possessed by memory CD4 T cells. However, there remains no direct demonstration of in vivo–generated resting memory CD4 T-cell conversion to a different subtype on secondary antigen challenge in vivo in an intact animal at the single-cell level. What has been clearly shown is that CD4 T cells possess extraordinary capacity for phenotypic heterogeneity, but that is a distinct property from plasticity. Heterogeneity is diversity of the resting memory CD4 T-cell population, not conversion of a single differentiated cell into another subtype. Apparently, plasticity at the population level can be accomplished by either mechanism, as heterogeneity of CD4 T-cell subpopulations could affect large shifts in subtype distribution at the overall population level via differential exponential expansion and death.

Great Debates

What are the most interesting topics likely to come up over dinner or drinks with your colleagues? Or, more importantly, what are the topics that don't come up because they are a little too controversial? In Immune Memory and Vaccines: Great Debates, Editors Rafi Ahmed and Shane Crotty have put together a collection of articles on such questions, written by thought leaders in these fields, with the freedom to talk about the issues as they see fit. This short, innovative format aims to bring a fresh perspective by encouraging authors to be opinionated, focus on what is most interesting and current, and avoid restating introductory material covered in many other reviews.

The Editors posed 13 interesting questions critical for our understanding of vaccines and immune memory to a broad group of experts in the field. In each case, several different perspectives are provided. Note that while each author knew that there were additional scientists addressing the same question, they did not know who these authors were, which ensured the independence of the opinions and perspectives expressed in each article. Our hope is that readers enjoy these articles and that they trigger many more conversations on these important topics.

Here, plasticity is defined as the conversion of a single cell possessing a well-characterized CD4 T-cell type into a cell no longer possessing that phenotype and instead possessing a different well-characterized CD4 T-cell phenotype. For example, conversion of a memory TH1 cell (T-bet+IFN-γ+CXCR5−Bcl6−) into a TFH cell (Bcl6+CXCR5+T-bet−IFN-γ−) would be plasticity. Separately, heterogeneity within a well-characterized CD4 T-cell population is defined here as a collection of varied phenotypes (<100% of the cell population) linked by a shared core phenotype. For example, heterogeneity among TH1 cells can be observed by flow cytometry or mass cytometry by defining TH1 cells as T-bet+IFN-γ+ cells and then observing fractions of the population expressing tumor necrosis factor (TNF), or interleukin (IL)-2, or Blimp1, or IL-10, or Eomes, etc. As another example, heterogeneity among TH2 cells can be observed by flow cytometry or mass cytometry by defining TH2 cells as GATA3hi cells and then observing fractions of the population expressing IL-5, IL-4, IL-13, CRTH2, CCR4, or IL-10, etc. As another example, heterogeneity among germinal center (GC) TFH cells can be observed by flow cytometry or mass cytometry by defining GC TFH cells as Bcl6+CXCR5+ cells and then observing fractions of the population expressing CXCL13, IL-21, IL-4, or CXCR3, etc. (Crotty 2014; Vinuesa et al. 2016). Heterogeneity at the whole population level further includes the range of differentiated CD4 T-cell subtypes present, including TH1, TH2, TH17, TH1*, CD4 CTL, TH9, and TFH cells, and perhaps even some form of “unbiased” TH0-type cells. Both plasticity and heterogeneity must be described based on analyses at the single-cell level.

Reports of T-cell program plasticity are unconvincing when the data are population-level changes in phenotypes. Such results can easily be the outcome of outgrowth of a minor cell population to become the dominant cell population, or vice versa, particularly given the exponential proliferation that T cells are capable of. Also unconvincing are the relevance of reports of cell program plasticity for which the central experiments are cell transfers into new hosts, particularly new hosts that are severely immunocompromised (e.g., T-cell-deficient mice). Such experiments show that CD4 T-cell plasticity can occur under extreme conditions, but the experiments have no demonstrated relevance to what CD4 T cells actually do or experience in an intact animal. In contrast, if transferred cells do maintain stability, those results are more credible, because they show stability of cell identity even when exposed to nonphysiological stresses. Apparent plasticity of differentiated CD4 T cells in vitro is generally not convincing, both because the in vitro experiments lack demonstrated in vivo relevance and because the experiments are performed at the cell population level, masking the impact of outgrowth of minor cell populations. The strictest criterion for demonstration of plasticity is the use of a lineage marker reporter transgenic mouse, tracking, over time, cells marked irreversibly. Such an experiment directly establishes the transcriptional history of a given cell. Many lineage-tracking experiments have been performed on nTregs, making use of Foxp3-IRES-GFP/YFP/RFP-Cre-based designs (Rubtsov et al. 2008, 2010; Zhou et al. 2009; Miyao et al. 2012). The central conclusions from the two later studies with more sophisticated modified Foxp3 gene reporter constructs was that Foxp3+ nTregs are very stable, with almost no plasticity (Rubtsov et al. 2010; Miyao et al. 2012). In contrast, substantial gene-expression heterogeneity could be observed in conditions of stress and while still maintaining core Foxp3+ nTreg programming. Still, the stability conclusions drawn from such studies are not necessarily directly transferrable for antigen-specific CD4 T-cell responses and CD4 T-cell memory, because nTregs develop their initial programming during thymic development.

STABILITY DURING A PRIMARY RESPONSE

There are no lineage marker reporter mouse studies showing plasticity of TH1, TH2, TH17, or TFH cells during a primary immune response in an intact animal. Thus, excluding thymic-derived Tregs, there is no definitive evidence of physiologically relevant CD4 T-cell plasticity during a primary immune response. Cell-transfer experiments have attempted to address stability or plasticity of antigen-specific CD4 T cells during a primary immune response. We observed that TFH and TH1 cells during a viral infection establish largely irreversible cell fates by 72 h postinfection, based on cell transfers of virus-specific TH1 or TFH cells from virally infected mice into time-matched virally infected mice (Choi et al. 2013). Similar pronounced cell-fate commitment results were independently reported using a protein immunization and an RFP-Bcl6 reporter mouse strain when transferring CXCR5−Bcl6− or CXCR5+Bcl6+ cells at day 7 postinfection (Liu et al. 2012). Plasticity of TH1 and TH2 cells to become TFH cells has been reported; however, those experiments used in vitro–generated TH1 and TH2 cells transferred into mice (Liu et al. 2012) or in vitro polarized cells then repolarized under different in vitro conditions (Lu et al. 2011). It is almost certainly the case that there is a window of time early during effector CD4 T-cell differentiation in a primary immune response when a given CD4 T cell possesses pluripotency, simultaneously expresses lineage-defining transcription factors (e.g., Bcl6 and T-bet and RORγt) (Nakayamada et al. 2011; Oestreich et al. 2012), and maintains the capacity to respond to different extrinsic signals and subsequently commit to one differentiated cell type (e.g., TFH or TH1 or TH17) (DuPage and Bluestone 2016). Thus, simple questions regarding durable stability versus plasticity must be assessed after that point, which is nontrivial to accomplish.

STABILITY DURING TRANSITION FROM EFFECTOR CELL TO MEMORY CELL

The transition from an effector CD4 T cell to a central memory CD4 T cell appears to also be a transition from a cell with a highly polarized gene-expression program to a cell with a less polarized gene-expression program. This may be key to understanding the apparent plasticity of memory CD4 T cells, discussed below.

Based on single-cell transfer studies in mouse model systems, most CD4 T-cell clones are capable of generating memory cells (Tubo et al. 2016), and a given individual CD4 T-cell clone can differentiate into multiple different CD4 T-cell types (e.g., TFH and TH1) as they divide during a primary immune response (Tubo et al. 2013). Furthermore, those effector cells can then develop into memory TFH and TH1 cells in frequencies comparable with the frequencies of TFH and TH1 cells generated by that clone during the effector phase of the CD4 T-cell response (Tubo et al. 2016). Human T-cell receptor (TCR) sequencing clonotype analysis of antigen-specific human memory CD4 T cells has shown that a given TCR sequence can be found in TH1, TH2, and TH17 antigen-specific central memory cells (Becattini et al. 2015), consistent with the mouse model observation.

During a primary immune response, it has been observed that TFH cells can have gene expression of other T-cell differentiation programs. In the context of a mouse with an acute lymphocytic choriomeningitis virus (LCMV) infection, the mantle TFH cells (mTFH, outside of GCs) and GC TFH cells express T-bet and interferon γ (IFN-γ) at substantially higher amounts than naïve CD4 T cells (Johnston et al. 2009; Yusuf et al. 2010; Ray et al. 2015). In our first paper on TFH cells, we stated “it is notable that T-bet and IFN-γ were still expressed in the TFH in vivo, although at lower levels than in TH1/non-TFH LCMV-specific CD4 T cells. These observations are consistent with a model in which TFH cells follow their own differentiation pathway but are not an isolated lineage and can show partial characteristics of TH1/TH2 polarization depending on environmental conditions.” Similar observations have been made for simian immunodeficiency virus (SIV) infection of rhesus macaques (Iyer et al. 2015). Given that both LCMV and SIV infections are extreme TH1-biased immune responses, the presence of TH1 gene expression by TFH cells in LCMV, and SIV immune response may represent uncommon exceptions. In support of that concept, human tonsillar GC TFH cells expressing TH1, TH2, or TH17 cytokines are rarely observed (Ma et al. 2009; Yu et al. 2009; Dan et al. 2016; Havenar-Daughton et al. 2016). For example, <1% of GC TFH cells produce IL-17 (Yu et al. 2009; Wong et al. 2015; Dan et al. 2016). Mouse GC TFH cells rarely produce IL-13, even under very strong TH2 polarizing helminth infection conditions (Liang et al. 2012), or house dust mite sensitization (Ballesteros-Tato et al. 2016) (TFH cells normally express IL-4 as part of canonical TFH programming, distinct and independent of TH2 programming, and thus TFH expression of IL-4 is not an indication of any TH2 gene programming [Crotty 2011]). Furthermore, Bcl6 represses many TH1, TH2, and TH17 genes and can prevent TH1 differentiation (Johnston et al. 2009; Oestreich et al. 2012; Hatzi et al. 2015). A counterargument can now be made that cytokine expression by intracellular cytokine staining is insufficiently sensitive to determine whether a GC TFH cell may possess TH1, or TH2, or TH17 gene expression, because GC TFH cells are intrinsically stingy cytokine producers, and intracellular cytokine staining missed ∼98% of human or macaque antigen-specific GC TFH cells (Dan et al. 2016; Havenar-Daughton et al. 2016). Thus, single-cell RNAseq of GC TFH cells may be required to better understand whether GC TFH cells with partial TH1, TH2, or TH17 heterogeneity characteristics are common or rare.

Considering the process from the opposite direction, it is clear that human memory TFH cells can show certain phenotypic markers commonly associated with TH1, TH2, or TH17 cells (discussed more in the next section). What is the ontogeny of those cells? One possibility is that TFH cells can be imprinted with a fractional amount of TH1 gene programming by an antigen-presenting cell during the early stages of T-cell priming in response to a TH1 pathogen, and, although that gene-expression program is efficiently squelched by Bcl6 in the effector mTFH and GC TFH progeny of that cell, a partial TH1 program remains imprinted (Fig. 1). As a GC TFH cell transitions into a memory cell, it loses expression of Bcl6 protein (Kitano et al. 2011; Liu et al. 2012; Choi et al. 2013; Hale et al. 2013; Locci et al. 2013; Ise et al. 2014), thus derepressing a range of genes, including Ccr7 and Il7ra (Fig. 1) (Kitano et al. 2011; Choi et al. 2013). One possibility is that the loss of Bcl6 protein as the GC TFH cell transitions into a memory TFH cell can allow a partial TH1 program imprinted at the time of T-cell priming to then become derepressed in some cells in the absence of Bcl6 protein, resulting in the central memory TFH cell acquiring a partially mixed TFH/TH1 phenotype (Fig. 1, model 1). Such an event may be considered “imprinted partial plasticity” because the phenotype of the cell would be dependent on the initial signals it received during T-cell priming, even if the gene-expression program was kept silent for long periods of time. If the cell were to subsequently become a TH1 cell and lose TFH characteristics, that would be “imprinted full plasticity.” This concept has not been directly tested.

Figure 1.

Figure 1.

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Three models of the development of heterogeneous or plastic CD4 T-cell memory. Each model is discussed in the main text.

A second possibility (model 2) is that a GC TFH cell that does not have any TH1, TH2, or TH17 gene expression or imprinting may be induced to activate a partial TH1, TH2, or TH17 gene-expression program if there are TH1, TH2, or TH17 differentiation inductive signals still present in the environment when the GC TFH cell is transitioning to become a memory cell and losing Bcl6 protein expression (Fig. 1). Such an event would be CD4 T-cell plasticity, termed “de novo partial plasticity” here, to distinguish it from imprinted plasticity.

Alternatively, a substantial proportion of memory TFH cells may be generated very early during an immune response derived from mantle TFH cells (mTFH) without going through a GC TFH cell stage. Evidence for such a pathway comes from studies of Sh2d1a−/− mice and humans, which have CD4 T cells that can differentiate into mTFH cells but not GC TFH cells and have evidence of TFH cell memory (He et al. 2013). If memory TFH cells are generated via such a pathway in immunocompetent mice and humans, it is plausible that memory TFH cells derived from mTFH cells may be less polarized than memory TFH cells derived from GC TFH cells, with less fixed-fate programming, and therefore may be more likely to have mixed attributes of TFH and TH1 or other programs (model 3). As is the case for models 1 and 2, this concept has also not been directly tested.

Given the observations and models described above, the presence of CXCR3+ CXCR5+ memory CD4 T cells in human peripheral blood is consistent with differentiation models based on heterogeneity, imprinted partial plasticity, or de novo partial plasticity. CXCR3 is expressed by TH1 cells and is a direct target of T-bet, and the CXCR3+ CXCR5+ memory CD4 T cells in human peripheral blood cells are almost all capable of producing IFN-γ on stimulation (Morita et al. 2011; Bentebibel et al. 2013; Locci et al. 2013; Obeng-Adjei et al. 2015). Thus, CXCR3+ CXCR5+ memory CD4 T cells could have potentially derived from CXCR3+ GC TFH cells or mTFH cells (Iyer et al. 2015), or CXCR3− GC TFH cells or mTFH cells that were exposed to a TH1 environment while transitioning to memory TFH cells.

CD4 T-cell biology usually does not fit tidy single pathway models; heterogeneity of phenotypes and differentiation patterns are common, as this is likely important to confound pathogen immune evasion strategies (Crotty 2012). Thus, a new report is surprising, but perhaps should not have been. Development of TH2 cells (IL-5+ IL-13+ CXCR5−) in the lungs in response to a second exposure to house dust mite antigens was observed to be dependent on effector TFH cells, with multiple lines of evidence pointing to differentiation of GC TFH cells (CXCR5+PD-1hi IL-21+) into TH2 cells (Ballesteros-Tato et al. 2016). These appeared to be fully differentiated active GC TFH cells, to the best of the ability of the authors to sort a pure cell population, with the previously stated caveats. In contrast, a different group, using a different IL-21 reporter mouse, did not observe TFH cells to be precursors to TH2 cells (IL-33R+ IL-5+ IL-13+) in a similar house dust mite model (Coquet et al. 2015), but they did not gate on CXCR5+ cells for the cell sorts. The two groups also transferred cells at different times after the primary antigen exposure, which may result in tracking cells at different points of cell-fate commitment. When TFH→TH2 plasticity occurred, the TFH cells were taken 6 d after the primary immunizations (Ballesteros-Tato et al. 2016). In neither study were resting memory CD4 T cells used (cells without activation marker expression taken at >30 d after the last antigen exposure). Lineage-tracking models that do not depend on cell transfers are likely to be the only means of resolving such disparate observations.

MEMORY CD4 T-CELL PHENOTYPE HETEROGENEITY AND STABILITY DURING RESTING MEMORY

Resting memory CD4 T cells appear to be largely stable over time by major lineage-defining phenotypic markers, as shown for antigen-specific TFH, TH1, and TH2 cells in mouse models (Harrington et al. 2008; Hale et al. 2013; Hondowicz et al. 2016; Tubo et al. 2016). Human data support the same conclusion but the antigen-specific data are limited (Locci et al. 2013; Bancroft et al. 2016; Da Silva Antunes et al. 2017). No longitudinal data on antigen-specific resting memory CD4 T-cell phenotypes from individual human donors are available at single-cell flow-cytometric resolution, which is much needed to demonstrate memory CD4 T-cell subset stability.

TH17 cells may be an exception to memory. There remains little evidence showing clear demonstration of in vivo–generated TH17 memory cells in mice. TH17 memory was absent in intact mice in one longitudinal antigen-specific CD4 T-cell study (Pepper et al. 2010). A later paper reported TH17 memory, but it depended on transfer of in vitro–generated TH17 cells (Muranski et al. 2011). Although an Il17a lineage marking reporter mouse has been available for many years (Hirota et al. 2011), there is a lack of publications on in vivo–generated TH17 memory. In humans, presence of antigen-experienced TH17 cells to Candida albicans has been clearly demonstrated (Zielinski et al. 2012); however, recurrent or continual exposure was not excluded, and a resting memory TH17 phenotype (e.g., Ki67−) was not shown for C. albicans–specific cells. Thus, although the existence of resting stable memory TH17 cells seems biologically reasonable and there is indirectly supportive literature (Lindenstrøm et al. 2012; McGeachy 2013), data showing antigen-specific resting memory TH17 cells with single-cell analysis are currently quite limited.

Although there has been evidence of heterogeneity within CD4 T-cell subsets, going back to early descriptions of TH1 and TH2 cells (e.g., TH2 cells producing some or all of IL-4, IL-5, IL-13, and IL-10), the vastness of the dimensional space of memory T-cell phenotypic heterogeneity was first made evident by the human memory CD8 T-cell mass spectrometry study of Newell and Davis (Newell et al. 2012). There was such phenotypic variety of memory CD8 T cells to influenza and cytomegalovirus (CMV) that the authors did not even attempt to put a number on the total range of memory CD8 T-cell phenotypes observed; instead, the diversity was best calculated as large spaces of phenotypic variation in three-dimensional principal component analysis (PCA) plots (Newell et al. 2012). Subsequent mass spectrometry analysis of human CD4 T cells has shown even more heterogeneity (Wong et al. 2015), consistent with the diversity of TH1, TH2, TH17, TFH, TH9, TH1*, CD4 CTL, and iTreg biology. Importantly, phenotypic diversity in human memory CD4 T cells is seen even at the level of individual TCR clonotypes (Becattini et al. 2015). One example of heterogeneity is the presence or absence of PD-1 expression by resting memory TFH cells (Locci et al. 2013). Heterogeneity is clearly present among chemokine receptor expression by memory CD4 T cells. A population of CCR6+ CXCR5+ resting memory CD4 T cells is present in human peripheral blood, and it has been suggested those cells represent “TFH17” memory cells (Morita et al. 2011). However, CCR6 expression is not specific to TH17 cells and does not correlate well with TH17 programming in many cases. One report found no IL-17a expression by stimulated CCR6+ CXCR5+ memory CD4 cells in single-cell analysis (Wong et al. 2015). Therefore, most CCR6 expression by memory TFH cells may be unrelated to TH17 biology and simply reflective of preferential chemotaxis needs. A similar situation exists for “TFH2” memory cells. “TFH2” memory CD4 T cells in human peripheral blood are commonly defined only on the basis of negative markers (CXCR3−CCR6−) (Morita et al. 2011; Jacquemin et al. 2015), based on the assumption that all TFH memory cells must have a TH1, TH2, or TH17 bias (which has certainly not been demonstrated at the single-cell level for GC TFH cells or resting memory TFH cells); thus, the concept of TFH2 cells remains poorly defined. Given that TFH cells produce IL-4 in a TFH-specific manner (independent of the TH2 program) (Crotty 2011), identification of a TFH2 cell requires demonstration of a resting memory CXCR5+ CD4 T cell capable of producing IL-5 and/or IL-13 at the single-cell level—a criterion not reached in the current literature. Such cells may exist, but they have not been shown directly, and they may be a minor fraction of the CXCR3−CCR6− memory TFH cells. There is clearly vast phenotypic heterogeneity among resting memory CD4 T cells, including resting memory TFH cells. However, at the level of cytokine production, it is still unknown how rare memory TFH cells with cytokine production attributes of other CD4 T-cell subsets are, except for overlap between TH1 and TFH programs in memory CD4 T cells, which has been clearly shown by multiple groups.

PLASTICITY WHEN MEMORY CD4 T CELLS ARE RECALLED?

A first study using an IL-21-GFP reporter mouse observed extensive plasticity of IL-21-GFP+ CXCR5+ TFH or IL-21-GFP− CXCR5+ cells after transfer into new hosts, with <50% of the memory cells maintaining CXCR5 expression, and a majority of the cells observed after recall by influenza infection were CXCR5− (Lüthje et al. 2012). A newer IL-21 fluorescent protein reporter mouse model observed robust stability of IL-21+ CXCR5+ TFH CD4 T cells after transfers into new hosts, but did not test memory time points (Weinstein et al. 2016). In the context of an acute LCMV infection, memory CD4 T cells appear to largely maintain their TH1 or TFH programming upon 2° response. TCR transgenic memory CD4 T cells with a resting TH1 phenotype all became effector TH1 cells (T-bethiBcl6loCXCR5−Granzyme B+) when transferred into a new host that was then infected with LCMV. In the same study, the major of memory CD4 T cells with a resting TFH phenotype became effector TFH and GC TFH cells on rechallenge (T-betloBcl6+CXCR5+Granzyme B−). A fraction of the memory TFH cells did lose CXCR5 in the 2° response, but because those experiments depended on cell transfers, it is unknown whether all of the memory TFH cells would have maintained their TFH program on 2° challenge under physiological conditions in an intact animal where they were allowed to maintain their normal localization. It is, of course, also formally possible that the memory cells would have showed even more plasticity if studying in an intact host. Another longstanding challenge with sorted cells that have high proliferative capacity such as lymphocytes is that even a 1% CXCR5− contamination of sorted cells could subsequently expand extensively during the exponential proliferation following 2° challenge, thus confounding cell-fate interpretations of cell-transfer experiments.

Resting central memory CD4 T cells show less polarized features than actively responding effector cells. Resting central memory TH1 or TFH cells have less active transcription and protein expression of many signature features of activated TH1 cells or activated GC TFH cells. For example, central memory TH1 cells express low levels of T-bet compared with effector cells, and resting memory TFH cells express Bcl6 but at levels indistinguishable from other central memory cells. Intuitively, one expects such cells to be prone to plasticity. Programmed chromatin modifications may prevent plasticity. Memory TFH cells rapidly up-regulate Bcl6 in vivo on restimulation (Ise et al. 2014). It is unknown whether memory TFH cells with mixed TFH–TH1 phenotypes (CXCR3+CXCR5+) differentiate into TFH (CXCR3−CXCR5+Bcl6+T-bet−), TFH1 (CXCR3+CXCR5+Bcl6+T-bet+), and/or conventional TH1 cells (CXCR3+CXCR5−Bcl6−T-bet+) in vivo in an intact mouse or human (Fig. 1). Evidence of plasticity was not observed in humans at the level of the overall CD4 T-cell response to pertussis, wherein the whole-cell pertussis vaccine and the acellular pertussis vaccine elicit predominantly TH1 and TH2 polarizing responses, respectively, and reimmunization with the acellular pertussis vaccine elicits a CD4 T-cell recall response with the same TH1 or TH2 characteristics for whichever vaccine the individual was initially immunized (Bancroft et al. 2016).

CONCLUDING REMARKS

There remains no direct demonstration of in vivo–generated resting memory CD4 T-cell conversion to a different subtype on secondary antigen challenge in vivo in an intact animal at the single-cell level. Lineage tracing experiments are needed to directly test whether plasticity occurs and, more importantly, how common or rare the process is under physiological conditions. Unfortunately, proper lineage-tracking genetic models are difficult to generate for TFH, TH17, and TH2 cells. The lineage-defining transcription factors for TFH, TH17, and TH2 cells are Bcl6, RORγT, and GATA3. GATA3 is constitutively expressed by all CD4 T cells; TH2 cells are defined by high GATA3 expression. Thus, a conventional GATA3 lineage reporter would mark all T cells. Therefore, development of a high-fidelity TH2 lineage genetic marker is a difficult challenge. A successful TH2 lineage genetic marker would probably need to depend on transcription from a gene or locus other than GATA3. Bcl6 is highly expressed by thymocytes, and thus a Bcl6 lineage reporter mouse constructed based on standard designs would be expected to mark most T cells. RORγt is also expressed by thymocytes and, thus, an RORγt lineage reporter mouse based on standard designs would be expected to mark most T cells. Therefore, a successful TFH or TH17 lineage genetic marker would probably need to depend on transcription from a gene or locus other than GATA3; IL-17a is one candidate for TH17 cells (Hirota et al. 2011). CXCR5 may be a good candidate for TFH cells. As for TH1 cells, a T-bet lineage reporter should be useful, but a key caveat is that transient expression of T-bet early on by a naïve CD4 T cell after activation is common and may have no influence on the future history of the cell. The same caveat applies for all constitutively active lineage marker systems, and contributed to controversy over the ontogeny of “exTregs.” Transient expression of Foxp3 by some cells may result in erroneous conclusions on the basis of very transient Cre expression (Rubtsov et al. 2008; Zhou et al. 2009; Miyao et al. 2012). This concern may be avoided by using estrogen-responsive Cre protein (Rubtsov et al. 2010).

As an alternative to lineage-tracking genetic markers, single-cell transfers into infection-matched hosts may be the best test of stability versus plasticity in a recall response. Such experiments would need to show that transferred cells (not using single-cell transfers) would localize in the new host to the same regions of lymph node (LN) and spleen as untransferred antigen-specific resting memory CD4 T cells of that subtype (e.g., proper microanatomical localization of memory TFH cells posttransfer).

In the end, it is unknown whether the appearance of plasticity by memory CD4 T cells at the population level is predominantly because of heterogeneity and outgrowth of subpopulations or predominantly attributable to plasticity.

It remains possible that true memory CD4 T-cell plasticity may be primarily of interest for purely academic reasons, as a memory recall response with apparent plasticity at the whole-cell population level could presumably be accomplished via rapid outgrown of a very minor population of memory cells. Because essentially all in vivo CD4 T-cell responses involve a mixture of subtypes (TH1, TH2, TH CTL, and TFH, for example), that scenario is plausible.

ACKNOWLEDGMENTS

This work is funded by the National Institutes of Health (NIH) National Institute of Allergy and Infectious Diseases (NIAID) R01 (S.C.). Thanks to Daniel DiToro for helpful discussions.

Footnotes

Editors: Shane Crotty and Rafi Ahmed

Additional Perspectives on Immune Memory and Vaccines: Great Debates available at www.cshperspectives.org

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